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Cooperative Agent Systems: Artificial Agents Play the Ultimatum Game

Author

Listed:
  • Fang Zhong

    (LeBow College of Business, Drexel University)

  • Steven O. Kimbrough

    (University of Pennsylvania)

  • D.J. Wu

    (LeBow College of Business, Drexel University)

Abstract

We explore computational approaches for artificial agents to play the ultimatum game. We compare our agents' behavior with that predicted by classical game theory, as well as behavior found in experimental (or behavioral) economics investigations. In particular, we study the following questions: How do artificial agents perform in playing the ultimatum game against fixed rules, dynamic rules, and rotating rules? How do coevolving artificial agents perform? Will learning software agents do better? What is the value of intelligence? What will happen when smart learning agents play against dumb (no-learning) agents? What will be the impact of agent memory size on performance? This exploratory study provides experimental results pertaining to these questions.

Suggested Citation

  • Fang Zhong & Steven O. Kimbrough & D.J. Wu, 2002. "Cooperative Agent Systems: Artificial Agents Play the Ultimatum Game," Group Decision and Negotiation, Springer, vol. 11(6), pages 433-447, November.
    • Handle: RePEc:spr:grdene:v:11:y:2002:i:6:d:10.1023_a:1020687015632
    DOI: 10.1023/A:1020687015632
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    References listed on IDEAS

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    Cited by:

    1. Jens Gudmundsson & Jens Leth Hougaard, 2020. "Enabling reciprocity through blockchain design," IFRO Working Paper 2020/14, University of Copenhagen, Department of Food and Resource Economics, revised 09 Feb 2021.

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